Methods and systems for retrospective internal gating

ABSTRACT

The present invention, in one form, is a method for deriving respiratory gated PET image reconstruction from raw PET data. In reconstructing the respiratory gated images in accordance with the present invention, respiratory motion information derived from individual voxel signal fluctuations, is used in combination to create usable respiratory phase information. Employing this method allows the respiratory gated PET images to be reconstructed from PET data with out the use of external hardware, and in a fully automated manner.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 16/395,201, filed Apr. 25, 2019, which is a continuation ofU.S. patent application Ser. No. 16/178,332, filed Nov. 1, 2018 (nowU.S. Pat. No. 10,448,903), which is a continuation of U.S. patentapplication Ser. No. 15/728,373, filed Oct. 9, 2017 (now U.S. Pat. No.10,117,625), which is a continuation of U.S. patent application Ser. No.12/151,121, filed May 5, 2008 (now U.S. Pat. No. 9,814,431), whichclaims priority to U.S. Provisional Patent Application 60/916,200, filedMay 4, 2007, the entire contents of each of which are incorporatedherein by reference.

BACKGROUND OF THE INVENTION

This invention relates generally to imaging systems and methods and moreparticularly to systems and methods for retrospective internal gating.

A large source of image degradation in medical imaging can be attributedto patient motion during the image acquisition, which causes loss ofdetail in the resultant images. For example respiratory motion causesblurring of the torso. This blurring can be difficult to characterize,and effectively can limit detectability of details, such as smalllesions or lesions with low contrasts, and might reduce the accuracy ofthe measurements for the lesions which are visible.

Respiratory gating in is an approach to lessen the image degradationfrom respiratory motion by separating the breathing cycle into differentphases and generating images from data corresponding to each of thesephases. In the past few years there has been much research in developingthis approach to imaging, with the hope that this can increase thequality diagnostic information derived from the images. The consensus inliterature is that the respiratory gating of images presents a feasiblesolution to the image degradation introduced by respiratory motion.Researchers have studied the use of respiratory gated PET with respectto improving image quantification, lesion detectability and artifacts,image-coregistration accuracy, and the use of gated PET/CT inradiotherapy treatment planning. A variety of methods has been presentedin the above literature for characterizing patient respiratory motionincluding techniques utilizing cameras, pressure belts, thermometers,point sources, pneumatic sensor systems, and mechanical ventilation (indogs).

In addition to the above work, which used hardware derived respiratorysignals, several software based methods have been proposed which utilizecharacterization of structural movement to gate the scans.

Acquiring and using software derived respiratory signals have severaladvantages over hardware based methods. The algorithms are image based,and thus machine independent, and can be used with existing scans, orscanners. What's more, if the algorithm is fully automated then thegated images can be generated without any extra effort or deviation fromroutine clinical procedures. They come at no additional “cost”, otherthan processing time, and can be generated along side traditionalnon-gated images.

Another advantage of using image based methods is that they provideassurance of the temporal alignment of the respiratory trace and theimage data.

BRIEF DESCRIPTION OF THE INVENTION

In one aspect, a method for retrospective internal gating is described.The method includes acquiring images at different times t1 . . . tn, andidentifying temporally cyclical signals, which are combined to create atime varying object motion function which correlates times t1 . . . tnand the phases of the periodic motion.

In another aspect, a computer-readable medium encoded with a program isdescribed. The program is configured to acquire images at differenttimes t1 . . . tn, and identify temporally cyclical signals, which arecombined to create a time varying object motion function whichcorrelates times t1 . . . tn and the phases of the periodic motion.

In yet another aspect, a computer is described. The computer isconfigured to acquire images at different times t1 . . . tn, andidentify temporally cyclical signals, which are combined to create atime varying object motion function which correlates times t1 . . . tnand the phases of the periodic motion.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: A flowchart of an embodiment of a method for retrospectiveinternal gating

FIG. 2: Simulated time-activity curve for single voxel (noiselesssimulation). (Top left) Image volume projections, (Top right)illustration of sample voxel, (bottom) time-activity curve of voxel.Projection 28 corresponds with a coronal projection (noiseless), andprojection 30 corresponds with a sagittal projection (noiseless).

FIG. 3: Flow chart summarizing main steps in image processing loop,illustrated for one possible embodiment with example curves. With eachnew voxel processed, the time varying object motion function is updated.(SD=standard deviation).

DETAILED DESCRIPTION OF THE INVENTION

Contemporary medical imaging produces 2D or 3D representations ofpatient anatomy or biological function. Several common type of medicalimaging devices are Computed Tomography, Positron Emission tomography,Magnetic resonance imaging.

In Computed Tomography (CT), an x-ray source and a detector are rotatedaround a patient, within the imaging plane, and projections measured bythe detector are gathered at various angles. These projections can thenused in a reconstruction algorithm, to generate images spatially mappingattenuation characteristics of the patient.

In Positron Emmision Tomography (PET), a patient is administered aradiopharmaceutical, and placed within the field of view of a fixed ringof detectors. The detectors measure the gamma rays resulting frompositron annihilation happening at the location of isotope. Areconstruction algorithm can then be applied to generate an image of theestimated spatial distribution of the radiopharmaceutical within thepatient.

In Magnetic Resonance Imaging, the magnetic moment of nuclei are placedwithin an oscillating magnetic field, and different characteristics ofthere behavior are used to generate information, allowing for thecreation of a anatomical or functional map. To achieve these images,information is spatially localized through the application of variationsin the applied magnetic field. These variations can be applied in theform of gradients leaving only a slice of anatomy on-resonance tocontribute to the signal.

Regardless of the imaging technique employed, all methods suffer fromartifacts relating to patient motion. Sources of motion includerespiration, and cardiac rhythms. Efforts have been made to createimages corrected for this motion.

As used herein, an element or step recited in the singular and precededwith the word “a” or “an” should be understood as not excluding pluralthe elements or steps, unless such exclusion is explicitly recited.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

Also as used herein, the phrase “reconstructing an image” is notintended to exclude embodiments of the present invention in which datarepresenting an image is generated but a viewable image is not.Therefore, as used herein the term “image” broadly refers to bothviewable images and data representing a viewable image. However, manyembodiments generate (or are configured to generate) at least oneviewable image.

Additionally, although the herein described methods are described in amedical setting, it is contemplated that the benefits of the methodsaccrue to non-medical imaging systems such as those systems typicallyemployed in an industrial setting or a transportation setting, such as,for example, but not limited to, a baggage scanning system for anairport, other transportation centers, government buildings, officebuildings, and the like. The benefits also accrue to micro PET and CTsystems which are sized to study lab animals as opposed to humans.

FIG. 1 illustrates an embodiment of a method for retrospective internalgating. The method includes acquiring 10 digital images i1 . . . in attimes t1 . . . tn to obtain a chronologically ordered image set, wheretemporal sampling is short relative to periodic motion being studied,and no greater than one half the expected period. Images may be realizedusing any form of imaging system. As an example, images i1 . . . in are3D PET images acquired at periods corresponding to 0.5 second timewindows, which is a plausible bin time to account for signal from humanrespiration. Examples of motion information include respiratory motioninformation and cardiac motion information.

FIG. 2 offers visualization of time-activity 12 information specific fora sample individual voxel. This time-activity 26 is derived byorganizing into a single discrete array the values v1 . . . vn 24 of anindividual voxel 22 in the successive images i1 . . . in. The individualvoxel locations in each individual image represent a volumes of space attime ti. The voxel values represent the signal of interest in the image,i.e. for PET the value would represent radioactivity concentration.

Voxel weighting factors 14 can be assigned to individual voxelsestablishing their importance during processing 18. In one embodiment,the weighting factor can be based upon the mean value of that voxel's 22time-activity 26 information, values v1 . . . vn 24. In anotherembodiment that weighting factor can be based upon proximity to spatialactivity gradients apparent in the images being used. A weighting factorof 0 can also be applied to voxels that the algorithm need not spendtime processing. Weighting factors can be applied to some, none, or allvoxels.

Voxel time-activity 26 information contained in v1 . . . vn 24 may haveunwanted frequencies filtered out using frequency filters. For example,when methods are being used for respiratory gating, non respiratoryfrequencies (less than 2 seconds and greater than 15 seconds) can befiltered out or attenuated in the time-activity signals. This can bedone to reduce the effects of noise in the signal. Other possiblefilters can be envisioned, such as ramp filters and Gaussian filters.

Information is combined from many voxels' time-activity 26 values tocreate a time varying object motion function. This is achieved byevaluating voxels and their respective time-activity informationindividually.

In one embodiment, voxels can be prioritized for processing by theirweighting factors 14 defined earlier. The time varying object motionfunction is a summation of filtered individual voxel time-activity 26curves.

In one embodiment, the processing is initiated by defining the timevarying object motion function as the filtered time-activity values 30of the voxel with the highest priority determined by the weightingfactors 14. Subsequent filtered voxel time-activity values aresynthesized, in order of priority, into a time varying object motionfunction using the following steps, shown in FIG. 3:

1) The filtered time-activity values of the voxel are combined with thecurrent time varying object motion function in three possible scenarios36:

-   -   (A) time varying object motion function (unchanged)    -   (B) time varying object motion function+voxel time-activity        values    -   (C) time varying object motion function−voxel time-activity        values

2) Of the three, the scenario with the highest standard deviation ischosen to serve as the new time varying object motion function 38 (i.e.the function with the greatest difference between peaks and valleys).

3) Unless the stopping criteria are met 34, the process is repeated forthe next voxel.

With each iteration, and for each new voxel processed, the time varyingobject motion function 32 either remains the same, or is improved. Inone embodiment these iterations may be set a priori to stop after thefirst 500 voxels are processed. Or, in another embodiment, they may beslated to stop after processing the voxel with a weighting factor abovea specified threshold. In yet another embodiment, every voxel within theimage space may be processed. In still yet another embodiment, voxelsmay be processed until the time varying object motion function meets aset criterion.

The purpose of step (1) is to determine the best contribution anindividual voxel can make to the time varying object motion function.The scenarios using addition and subtraction are included to account forthe fact that voxels may be in or out of phase with the time varyingobject motion function, depending on whether they were positionedsuperior or inferior to gradients of motion. Other embodiments usingdifferent methods of evaluating step (1) above can be envisioned.

Once the chosen stopping criteria are met, the current time varyingobject motion function 40 is returned for use in the mapping of imagedata to phase of motion.

Final phase information 20 for the motion of the imaged object can beextracted from the timing of the peaks and dips in the time varyingobject motion function. In one embodiment, relating to respiratorymotion, local maxima and local minima on the time varying object motionfunction may be characterized as corresponding to the timing of fullinspiration and full expiration, respectively.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

What is claimed is:
 1. A method for retrospective internal gatingcomprising: acquiring image data corresponding to times t1 . . . tn tobe used to generate one or more images corresponding to a moving object;extracting information for a plurality of arrays derived from the imagedata; generating a time varying object motion function based on theextracted information for the plurality of arrays of the image data;determining, based on the time varying object motion function, phaseinformation for motion of the moving object; and generating at least oneimage correcting for the motion of the moving object based on thedetermined phase information for motion of the moving object and theacquired image data.
 2. The method of claim 1, wherein information isdefined as a signal of an array element over times t1 . . . tn.
 3. Themethod of claim 1, further comprising assigning weighting factors to theplurality of arrays, wherein the weighting factors include arrayspecific weighting factors based on the mean array activity over timest1 . . . tn.
 4. The method of claim 1, further comprising assigningweighting factors to the plurality of arrays, wherein the weightingfactors include array specific weighting factors based upon the array'sproximity to greater spatial signal gradients on a non-corrected image.5. The method of claim 4, wherein the non-corrected image is comprisedof combined image data from times t1 . . . tn.
 6. The method of claim 1,further comprising assigning weighting factors to the plurality ofarrays, wherein the weighting factors include array specific weightingfactors assigned based on magnitude of signal variation over times t1 .. . tn, for each array.
 7. The method of claim 1, wherein at least somearrays of the plurality of arrays are deemed unimportant and weighted atzero usage value.
 8. The method of claim 1, wherein information isfiltered in frequency space for windows encompassing expected validperiodicity of the motion.
 9. The method of claim 8, wherein thefrequency window used may be adjusted to be patient or data specific.10. The method of claim 8, wherein the frequency window may be adjustedover times t1 . . . tn.
 11. The method of claim 1, wherein arrayinformation is processed serially in order of prioritization to yield atime varying object motion function.
 12. The method of claim 11, whereinthe time varying object motion function spans t1 . . . tn.
 13. Themethod of claim 1, wherein the initial time varying object motionfunction is assigned to be the curve of the array with the highestpriority.
 14. The method of claim 1, wherein individual arrayinformation is combined with the evolving time varying object motionfunction in three possible scenarios: the first scenario is leaving thecurrent time varying object motion function unaltered, the secondscenario is adding the individual array information to the current timevarying object motion function, and the third scenario is subtractingthe individual array information from the current time varying objectmotion function, to account for possible phase mismatch; of these threescenarios, the one with the most significant improvement is chosen asthe new time varying object motion function, to be used in evaluation ofthe next voxel.
 15. The method of claim 1, wherein determining, based onthe time varying object motion function, the phase information formotion of the moving object is based upon identifying non-recurringpatterns in the time varying object motion function.
 16. The method ofclaim 1, wherein the determining, based on the time varying objectmotion function, the phase information for motion of the moving objectis based upon identifying recurring patterns in the time varying objectmotion function.
 17. The method of claim 1, further comprising mappingof image data to corresponding motion phases based on the time varyingobject motion function, wherein mapped image data is reordered andcategorized in such a way that images within a category all appear to betaken at the same phase of motion.
 18. The method of claim 1, whereinthe acquired data includes data for a respiratory cycle of a subjectcorresponding to the moving object, and wherein acquiring the image dataincludes acquiring the image data for at least one breath cycle of asubject corresponding to the moving object.
 19. A non-transitorycomputer-readable medium encoded with a program that when executed byone or more processors cause a machine to: acquire image datacorresponding to times t1 . . . tn to be used to generate one or moreimages corresponding to a moving object; extract information for aplurality of arrays derived from the image data; generate a time varyingobject motion function based on the extracted information for theplurality of arrays of the image data; determine, based on the timevarying object motion function, phase information for motion of themoving object; and generate at least one image correcting for the motionof the moving object based on the determined phase information formotion of the moving object and the acquired image data.
 20. A systemcomprising: one or more processors; and a non-transitorycomputer-readable medium having instructions stored thereon that whenexecuted by the one or more processors cause the system to: acquireimage data corresponding to times t1 . . . tn to be used to generate oneor more images corresponding to a moving object; extract information fora plurality of arrays derived from the image data; generate a timevarying object motion function based on the extracted information forthe plurality of arrays of the image data; determine, based on the timevarying object motion function, phase information for motion of themoving object; and generate at least one image correcting for the motionof the moving object based on the determined phase information formotion of the moving object and the acquired image data.